Leveraging extensive trajectory data to analyze the operation modes of agricultural machinery for gathering precise spatial information is an important fundamental task for subsequent agricultural machinery trajectory research. However, complex algorithm models hinder nonspecialized researchers from further processing agricultural machinery trajectory data. In the present application note, ChatGPT is taken as an example and a complete prompt guide for large language models (LLMs) is provided for autonomously identifying the operation mode of agricultural machinery. This guide provides low-cost workflows for processing agricultural machinery trajectory data when computer science or data science expertise is lacking. It even possesses the capability to utilize newly learned algorithms such as the random forest model, which has not been previously explored in the literature for operation mode identification, to accomplish the task. To the best of our knowledge, this is the first attempt to apply LLMs to identifying agricultural machinery operation modes based on trajectory data. The complete prompt guide is publicly available at https://github.com/kakushuu/prompt-guide/.

Due to varying crop maturity periods and uneven distribution of agricultural machinery, China has developed a unique service model known as cross-regional agricultural machinery operations. Currently, China’s comprehensive mechanization rate for grain crops is relatively high, creating a substantial market for cross-regional agricultural machinery operations. Research on the behavioral patterns of cross-regional agricultural machinery migration is both urgent and significant. Considering the actual rules of cross-regional migration during the wheat harvest and the characteristics of the trajectory data, this paper proposes a trajectory mining method using a recursive search-based DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. One representative finding of this study is that by mining the trajectory data of wheat harvesters within 25 d of peak harvest period, 131 cross-regional trajectories were identified, consisting of 11 633 harvesters. Three main routes of wheat harvester cross-regional migration were identified, along with several smaller routes outside their range. The overall spatiotemporal pattern aligns with observed realities in China. This study can provide valuable references for operators to optimize cross-regional routes, for agricultural machinery manufacturers to develop location-based services, and for relevant government departments to formulate policies.